Data processing is the most talked about topic of this year. From the figure below, you can comprehend that NoSQL and SQL databases are the ones most preferred by the respondents.
By putting together the percentage of respondents who found them fetching and who called them ‘extremely engaging’, we can conclude who the runner-up is. Here, NoSQL databases secure the second rank with 74.8%.
The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.
Below quote from Sherlock Holmes is relevant –
“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”
He always started each case by focusing on the problem.
The problem would sometimes arrive in the form of a letter, sometimes as an item in the newspaper, but most often, it would announce itself by a knock at the door. The client would then present the mystery to Holmes and he would probe the client for salient information. Holmes never relied on guesswork or on assumptions. For Holmes, each new case was unique, and what mattered were reliable and verifiable facts about the case. These gave the investigation an initial focus and direction.
“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
Similarly a data analyst is expected not to assume or formulate theories, which can make the reasoning biased. In his stories, Sherlock Holmes demonstrates his keen powers of observation and deduction from data in front of him. He can decipher how the light enters in Watson’s bathroom based on how his beard is shaved; he attests one person has lived in China from one of his tattoos; he discovers previous financial situation of a man who he had never seen before just looking to the hat the man had just used.
A data scientist has powerful computational and statistics tools that help him finding patterns amid so much data.
In the end, a data analyst’s introduction can be similar to what Sherlock said:
“My name is Sherlock Holmes. It is my business to know what other people do not
know.”
–Team Cosmos
You can learn more about Data analysis by taking up Data analyst certification courses. DexLab Analytics also offers Business analyst training courses.
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Do you follow your instincts instead of data and insights?
Do you prefer storing data in different databases, in separate formats with varying values?
Habits are subject to change. Though it may take some time, but eventually it evolves. Good and bad habits make a person. Good habits don’t demand attention, but bad habits often need to be looked into.
If you suffer from bad data habits, then you must make sure you deal with it. It has to be a thing from your past rather than a dominating present. After all, data is incredibly important for business organizations to proliferate and generate decent revenues.
As per Experian’s Data Quality Report, 83% of companies consider their revenue suffers from inaccurate and insufficient customer data. It happens because of time and money wastage on insubstantial resources, which leads to a humungous loss of productivity and profit.
Data is the essence of business. From email delivery to customer feedback to profit generation, the impact of data trickles from strata to strata.
Sadly, many companies fail to fathom the significance of data and continue storing data on multiple systems, instead of a single location, in various formats without actually knowing ways to handle it. This eventually results into huge data pile-ups, where the entire data silo becomes difficult to manage.
However, if you have the right tools and a zeal to ensure data quality, you can confidently manage your data, eradicate duplications and fix errors before they inflict damage to your fundamentals. Besides, prudent strategies, time-to-time reviews and absolute determination are necessary; read this article to gain more insights about how to work on your bad data habits.
Detailed information about customers is crucial for better assistance and quicker efficiency. So, you should always tell your customer support team to derive more information about their customers in order to serve better.
What data is important for your business? Once you know that, you will be able to apprehend your customer’s needs and expectations more effectively. Moreover, be sure that the data is accessible to all those who really needs it, otherwise it won’t be fruitful.
For high quality data, make sure you introduce standard data policies and procedures. Also, ensure that the people working in your organization are acquainted with the ways of recording and storing it.
Data degradation is common. Human beings commit mistakes. Hence, it is important to regularly review and cleanse data in order to avoid future discrepancies.
Integrate your network to ensure the data is stored on one server, but accessible from multiple locations. This will help you get an entire picture of your company’s business performance over varied mediums. Install any of the improved Data Cleaning Software to make sure your data is free of duplicates and perfectly formatted right from the start.
To brush up your analytics skills, get enrolled in a Data analyst course. Visit DexLab Analytics.
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Ever wondered why many organizations often find it hard to implement Big Data? The reason often is poor or non-existent data management strategies which works counterproductive.
Data cannot be delivered or analysed without proper technology systems and procedural flows data can never be analysed or delivered. And without an expert team to manage and maintain the setup, errors, and backlogs will be frequent.
Before we make a plan of the data management strategies we must consider what systems and technologies one may need to add and what improvements can be made to an existing processes; and what do these roles bring about in terms of effects with changes.
However, a much as is possible any type of changes should be done by making sure a strategy is going to be integrated with the existing business process.
And it is also important to take a holistic point of view, for data management. After all, a strategy that does not work for its users will never function effectively for any organization.
With all these things in mind, in this article we will examine each of the three most important non-data components for a successful data management strategy – this should include the process, the technology and the people.
There is a lot of technology implemented into the Big Data industry, and a lot of it is in the form of a highly specific tool system. Almost all of the enterprises do need the following types of tech:
This will isolate specific information from a large data sets and transform it into usable metrics. Some o the familiar data mining tools are SAS, R and KXEN.
The process of ETL is used to extract, transform, and also will load data so that it can be used. ETL tools also automate this process so that human users will not have to request data manually. Moreover, the automated process is way more consistent.
A centralised data warehouse will be able to store all of an organization’s data and also integrate a related data from other sources, this is an indispensible part of any data management plan. It also keeps data accessible, and associates a lot of kinds of customer data for a complete view.
These are tools, which provide a layer of security and quality assurance by monitoring some critical environments, with problem diagnosing, whenever they arise, and also to quickly notify the team behind analytics.
These are tools that turn processed data into insights, that are tailored to extract roles along with users. Data must go to the right people and in the right format for it to be useful.
And in analytics highly specific metrics are combined like customer acquisition data, product life cycle, and tracking details, with intuitive user friendly interfaces. They often integrate with some non-analytics tools to ensure the best possible user experience.
So, it is important to not think of the above technologies as simply isolated elements but instead consider them as a part of a team. Which must work together as an organized unit.
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To be a successful data analyst, you must share two very important attributes that you must possess:
That is because the universe around us deals in the common currency of information and wisdom, which should flow freely without any price tags on it.
Continue reading “You Must Put These Data Analytics Books in Your Reading List This Year”
These days leveraging data be it big or small has become a powerful tool for all enterprises. IT firms are successfully transitioning to digital businesses and opportunities within the companies themselves are increasing to fulfil the growing demands.
So, if you want to join this megatrend in the job market, read on to find out the most in-demand data analytics job titles for today’s professionals:
This job title has been getting a lot of attention since the past few years now. So much so, that even Glassdoor named it as the best career choice for optimum work/life balance. Their salaries are also comparatively higher.
But the field is still cloudy in terms of the job functions. So, let us understand what it actually means to be a data scientist.
According to Burch Works data scientists are people who “apply sophisticated quantitative measures and computer skills to both structure and analyze the massive amount of unstructured data sets or stream data continuously with an intention to derive information and prescribe action.
The executive recruiting firm says that the coding skills of these professionals are the main distinguishing factor that separates them from other predictive analytics professionals and allows them to exploit data regardless of its size, source and format.
These data professionals often have a master’s degree or a PhD in quantitative disciplines, such as applied math or statistics. They have expert skills and knowledge in statistical and machine learning methods and know tools like SAS, R etc. they are also proficient in other Big Data software like Hadoop and Spark.
The professionals with this job role perform predictive analysis, prescriptive analysis, simulations, and all other forms of advanced analytics. Their role is however, significantly different from data scientists as they do not work with very large data sets and also not with unstructured data.
A gamut of responsibilities fall under the job listings of a data analyst. They include ensuring data quality and governance, building different systems that enable businesses to gain user insights, performing actual data analysis and much more. However, the skill sets are similar and typically these professionals fit into the same category as advanced analytics professionals and data scientists, because they all can analyze data. But despite such similarities data analysts may be considered as more junior-level employees who are still in a way generalists and can fit into several different job roles within the organization.
These are the wizards who work behind the scenes to make the jobs of data analysts and data scientists easier. They are technical professionals who have a deep understanding of Hadoop and other Big Data technologies like MapReduce, Hive, SQL and Pig, NoSQL technologies and other data warehousing systems.
Their primary job role is to construct the plumbing, build the data pipelines that clean, collect and aggregate data, organize it from different sources and then load them in data warehouses and databases.
Note that data engineers do not analyze data, but in other words keep the data flowing for processing so that other professionals can analyze them.
Business analysts can perform all the tasks that are almost the same for those who perform data analysis. However, business analysts generally have specialized knowledge of their specific business domain and then they apply that knowledge and analysis specifically for the business operations. For example, they may use their analytical skills to recommend improvement suggestions for the business.
These professionals are responsible for all things relevant to the operations, monitoring, and maintenance of the databases, often SQL or other relational database management systems also form their jurisdiction. Their tasks include installation, configuration, schemas definition, user training, and maintaining documents.
The database vendors like IBM, Oracle, Microsoft and others often offer certifications specific to their own proprietary technologies for such pros.
BI professionals are responsible for adapting themselves with OLAP tools, reports and other data dashboards for looking at historical trends within data sets. Business Intelligence can have data visualization, and also include popular business intelligence platforms like Qlik, Tableau and Microsoft Power BI.
These were the most in-demand job titles in the data analysis industry, to help turn your career into the right direction take a look at our Big Data courses and have a job that you would thoroughly enjoy.
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With this Data analyst interview guide you will know what to expect in an interview round for a position of data analyst.
A good data analyst or scientist must be capable of drawing actionable insights from the data that a company generates. They must possess a good sense of what data they must collect and should have a solid process for carrying it out effectively using processes of data analysis and building predictive models.
A data analyst must possess a strong foundation in the following topics: operations research, statistics, machine learning along with some database skills, such as SQL or SAS in order to clean, retrieve and process the data from different sources. One can lead to this role from different pathways thus candidates can expect to be bombarded with questions relevant to statistics ort mathematics and even computer programming or engineering.
Data scientists are also often required to script programs using R or Python or Matlab and the role will typically not place emphasis on the programming skills or practices and the general software engineering skills which is necessary for working with production quality software.
Hope this list of common data science interview questions will prepare you for a job at a reputable data analysis company. For more such data science news, tutorials and articles with emphasis on programming and analytics view our regular updates from DexLab Analytics.
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You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.
This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”